👤 Sisi Liu

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3182
Articles
1983
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Also published as: A Liu, Ai Liu, Ai-Guo Liu, Aidong Liu, Aiguo Liu, Aihua Liu, Aijun Liu, Ailing Liu, Aimin Liu, Allen P Liu, Aman Liu, An Liu, An-Qi Liu, Ang-Jun Liu, Anjing Liu, Anjun Liu, Ankang Liu, Anling Liu, Anmin Liu, Annuo Liu, Anshu Liu, Ao Liu, Aoxing Liu, B Liu, Baihui Liu, Baixue Liu, Baiyan Liu, Ban Liu, Bang Liu, Bang-Quan Liu, Bao Liu, Bao-Cheng Liu, Baogang Liu, Baohui Liu, Baolan Liu, Baoli Liu, Baoning Liu, Baoxin Liu, Baoyi Liu, Bei Liu, Beibei Liu, Ben Liu, Bi-Cheng Liu, Bi-Feng Liu, Bihao Liu, Bilin Liu, Bin Liu, Bing Liu, Bing-Wen Liu, Bingcheng Liu, Bingjie Liu, Bingwen Liu, Bingxiao Liu, Bingya Liu, Bingyu Liu, Binjie Liu, Bo Liu, Bo-Gong Liu, Bo-Han Liu, Boao Liu, Bolin Liu, Boling Liu, Boqun Liu, Bowen Liu, Boxiang Liu, Boxin Liu, Boya Liu, Boyang Liu, Brian Y Liu, C Liu, C M Liu, C Q Liu, C-T Liu, C-Y Liu, Caihong Liu, Cailing Liu, Caiyan Liu, Can Liu, Can-Zhao Liu, Catherine H Liu, Chan Liu, Chang Liu, Chang-Bin Liu, Chang-Hai Liu, Chang-Ming Liu, Chang-Pan Liu, Chang-Peng Liu, Changbin Liu, Changjiang Liu, Changliang Liu, Changming Liu, Changqing Liu, Changtie Liu, Changya Liu, Changyun Liu, Chao Liu, Chao-Ming Liu, Chaohong Liu, Chaoqi Liu, Chaoyi Liu, Chelsea Liu, Chen Liu, Chenchen Liu, Chendong Liu, Cheng Liu, Cheng-Li Liu, Cheng-Wu Liu, Cheng-Yong Liu, Cheng-Yun Liu, Chengbo Liu, Chenge Liu, Chengguo Liu, Chenghui Liu, Chengkun Liu, Chenglong Liu, Chengxiang Liu, Chengyao Liu, Chengyun Liu, Chenmiao Liu, Chenming Liu, Chenshu Liu, Chenxing Liu, Chenxu Liu, Chenxuan Liu, Chi Liu, Chia-Chen Liu, Chia-Hung Liu, Chia-Jen Liu, Chia-Yang Liu, Chia-Yu Liu, Chiang Liu, Chin-Chih Liu, Chin-Ching Liu, Chin-San Liu, Ching-Hsuan Liu, Ching-Ti Liu, Chong Liu, Christine S Liu, ChuHao Liu, Chuan Liu, Chuanfeng Liu, Chuanxin Liu, Chuanyang Liu, Chun Liu, Chun-Chi Liu, Chun-Feng Liu, Chun-Lei Liu, Chun-Ming Liu, Chun-Xiao Liu, Chun-Yu Liu, Chunchi Liu, Chundong Liu, Chunfeng Liu, Chung-Cheng Liu, Chung-Ji Liu, Chunhua Liu, Chunlei Liu, Chunliang Liu, Chunling Liu, Chunming Liu, Chunpeng Liu, Chunping Liu, Chunsheng Liu, Chunwei Liu, Chunxiao Liu, Chunyan Liu, Chunying Liu, Chunyu Liu, Cici Liu, Clarissa M Liu, Cong Cong Liu, Cong Liu, Congcong Liu, Cui Liu, Cui-Cui Liu, Cuicui Liu, Cuijie Liu, Cuilan Liu, Cun Liu, Cun-Fei Liu, D Liu, Da Liu, Da-Ren Liu, Daiyun Liu, Dajiang J Liu, Dan Liu, Dan-Ning Liu, Dandan Liu, Danhui Liu, Danping Liu, Dantong Liu, Danyang Liu, Danyong Liu, Daoshen Liu, David Liu, David R Liu, Dawei Liu, Daxu Liu, Dayong Liu, Dazhi Liu, De-Pei Liu, De-Shun Liu, Dechao Liu, Dehui Liu, Deliang Liu, Deng-Xiang Liu, Depei Liu, Deping Liu, Derek Liu, Deruo Liu, Desheng Liu, Dewu Liu, Dexi Liu, Deyao Liu, Deying Liu, Dezhen Liu, Di Liu, Didi Liu, Ding-Ming Liu, Dingding Liu, Dinglu Liu, Dingxiang Liu, Dong Liu, Dong-Yun Liu, Dongang Liu, Dongbo Liu, Dongfang Liu, Donghui Liu, Dongjuan Liu, Dongliang Liu, Dongmei Liu, Dongming Liu, Dongping Liu, Dongxian Liu, Dongxue Liu, Dongyan Liu, Dongyang Liu, Dongyao Liu, Dongzhou Liu, Dudu Liu, Dunjiang Liu, Edison Tak-Bun Liu, En-Qi Liu, Enbin Liu, Enlong Liu, Enqi Liu, Erdong Liu, Erfeng Liu, Erxiong Liu, F Liu, F Z Liu, Fan Liu, Fan-Jie Liu, Fang Liu, Fang-Zhou Liu, Fangli Liu, Fangmei Liu, Fangping Liu, Fangqi Liu, Fangzhou Liu, Fani Liu, Fayu Liu, Fei Liu, Feifan Liu, Feilong Liu, Feiyan Liu, Feiyang Liu, Feiye Liu, Fen Liu, Fendou Liu, Feng Liu, Feng-Ying Liu, Fengbin Liu, Fengchao Liu, Fengen Liu, Fengguo Liu, Fengjiao Liu, Fengjie Liu, Fengjuan Liu, Fengqiong Liu, Fengsong Liu, Fonda Liu, Foqiu Liu, Fu-Jun Liu, Fu-Tong Liu, Fubao Liu, Fuhao Liu, Fuhong Liu, Fujun Liu, Gan Liu, Gang Liu, Gangli Liu, Ganqiang Liu, Gaohua Liu, Ge Liu, Ge-Li Liu, Gen Sheng Liu, Geng Liu, Geng-Hao Liu, Geoffrey Liu, George E Liu, George Liu, Geroge Liu, Gexiu Liu, Gongguan Liu, Guang Liu, Guangbin Liu, Guangfan Liu, Guanghao Liu, Guangliang Liu, Guangqin Liu, Guangwei Liu, Guangxu Liu, Guannan Liu, Guantong Liu, Gui Yao Liu, Gui-Fen Liu, Gui-Jing Liu, Gui-Rong Liu, Guibo Liu, Guidong Liu, Guihong Liu, Guiju Liu, Guili Liu, Guiqiong Liu, Guiquan Liu, Guisheng Liu, Guiyou Liu, Guiyuan Liu, Guning Liu, Guo-Liang Liu, Guochang Liu, Guodong Liu, Guohao Liu, Guojun Liu, Guoke Liu, Guoliang Liu, Guopin Liu, Guoqiang Liu, Guoqing Liu, Guoquan Liu, Guowen Liu, Guoyong Liu, H Liu, Hai Feng Liu, Hai-Jing Liu, Hai-Xia Liu, Hai-Yan Liu, Haibin Liu, Haichao Liu, Haifei Liu, Haifeng Liu, Hailan Liu, Hailin Liu, Hailing Liu, Haitao Liu, Haiyan Liu, Haiyang Liu, Haiying Liu, Haizhao Liu, Han Liu, Han-Fu Liu, Han-Qi Liu, Hancong Liu, Hang Liu, Hanhan Liu, Hanjiao Liu, Hanjie Liu, Hanmin Liu, Hanqing Liu, Hanxiang Liu, Hanyuan Liu, Hao Liu, Haobin Liu, Haodong Liu, Haogang Liu, Haojie Liu, Haokun Liu, Haoling Liu, Haowei Liu, Haowen Liu, Haoyue Liu, He-Kun Liu, Hehe Liu, Hekun Liu, Heliang Liu, Heng Liu, Hengan Liu, Hengru Liu, Hengtong Liu, Heyi Liu, Hong Juan Liu, Hong Liu, Hong Wei Liu, Hong-Bin Liu, Hong-Li Liu, Hong-Liang Liu, Hong-Tao Liu, Hong-Xiang Liu, Hong-Ying Liu, Hongbin Liu, Hongbing Liu, Hongfa Liu, Honghan Liu, Honghe Liu, Hongjian Liu, Hongjie Liu, Hongjun Liu, Hongli Liu, Hongliang Liu, Hongmei Liu, Hongqun Liu, Hongtao Liu, Hongwei Liu, Hongxiang Liu, Hongxing Liu, Hongyan Liu, Hongyang Liu, Hongyao Liu, Hongyu Liu, Hongyuan Liu, Houbao Liu, Hsiao-Ching Liu, Hsiao-Sheng Liu, Hsiaowei Liu, Hsu-Hsiang Liu, Hu Liu, Hua Liu, Hua-Cheng Liu, Hua-Ge Liu, Huadong Liu, Huaizheng Liu, Huan Liu, Huan-Yu Liu, Huanhuan Liu, Huanliang Liu, Huanyi Liu, Huatao Liu, Huawei Liu, Huayang Liu, Huazhen Liu, Hui Liu, Hui-Chao Liu, Hui-Fang Liu, Hui-Guo Liu, Hui-Hui Liu, Hui-Xin Liu, Hui-Ying Liu, Huibin Liu, Huidi Liu, Huihua Liu, Huihui Liu, Huijuan Liu, Huijun Liu, Huikun Liu, Huiling Liu, Huimao Liu, Huimin Liu, Huiming Liu, Huina Liu, Huiping Liu, Huiqing Liu, Huisheng Liu, Huiying Liu, Huiyu Liu, Hulin Liu, J Liu, J R Liu, J W Liu, J X Liu, J Z Liu, James K C Liu, Jamie Liu, Jay Liu, Ji Liu, Ji-Kai Liu, Ji-Long Liu, Ji-Xing Liu, Ji-Xuan Liu, Ji-Yun Liu, Jia Liu, Jia-Cheng Liu, Jia-Jun Liu, Jia-Qian Liu, Jia-Yao Liu, JiaXi Liu, Jiabin Liu, Jiachen Liu, Jiahao Liu, Jiahua Liu, Jiahui Liu, Jiajie Liu, Jiajuan Liu, Jiakun Liu, Jiali Liu, Jialin Liu, Jiamin Liu, Jiaming Liu, Jian Liu, Jian-Jun Liu, Jian-Kun Liu, Jian-hong Liu, Jian-shu Liu, Jianan Liu, Jianbin Liu, Jianbo Liu, Jiandong Liu, Jianfang Liu, Jianfeng Liu, Jiang Liu, Jiangang Liu, Jiangbin Liu, Jianghong Liu, Jianghua Liu, Jiangjiang Liu, Jiangjin Liu, Jiangling Liu, Jiangxin Liu, Jiangyan Liu, Jianhua Liu, Jianhui Liu, Jiani Liu, Jianing Liu, Jianjiang Liu, Jianjun Liu, Jiankang Liu, Jiankun Liu, Jianlei Liu, Jianmei Liu, Jianmin Liu, Jiannan Liu, Jianping Liu, Jiantao Liu, Jianwei Liu, Jianxi Liu, Jianxin Liu, Jianyong Liu, Jianyu Liu, Jianyun Liu, Jiao Liu, Jiaojiao Liu, Jiaoyang Liu, Jiaqi Liu, Jiaqing Liu, Jiawen Liu, Jiaxian Liu, Jiaxiang Liu, Jiaxin Liu, Jiayan Liu, Jiayi Liu, Jiayin Liu, Jiaying Liu, Jiayu Liu, Jiayun Liu, Jiazhe Liu, Jiazheng Liu, Jiazhuo Liu, Jidan Liu, Jie Liu, Jie-Qing Liu, Jierong Liu, Jiewei Liu, Jiewen Liu, Jieying Liu, Jieyu Liu, Jihe Liu, Jiheng Liu, Jin Liu, Jin-Juan Liu, Jin-Qing Liu, Jinbao Liu, Jinbo Liu, Jincheng Liu, Jindi Liu, Jinfeng Liu, Jing Liu, Jing Min Liu, Jing-Crystal Liu, Jing-Hua Liu, Jing-Ying Liu, Jing-Yu Liu, Jingbo Liu, Jingchong Liu, Jingfang Liu, Jingfeng Liu, Jingfu Liu, Jinghui Liu, Jingjie Liu, Jingjing Liu, Jingmeng Liu, Jingmin Liu, Jingqi Liu, Jingquan Liu, Jingqun Liu, Jingsheng Liu, Jingwei Liu, Jingwen Liu, Jingxing Liu, Jingyi Liu, Jingying Liu, Jingyun Liu, Jingzhong Liu, Jinjie Liu, Jinlian Liu, Jinlong Liu, Jinman Liu, Jinpei Liu, Jinpeng Liu, Jinping Liu, Jinqin Liu, Jinrong Liu, Jinsheng Liu, Jinsong Liu, Jinsuo Liu, Jinxiang Liu, Jinxin Liu, Jinxing Liu, Jinyue Liu, Jinze Liu, Jinzhao Liu, Jinzhi Liu, Jiong Liu, Jishan Liu, Jitao Liu, Jiwei Liu, Jixin Liu, Jonathan Liu, Joyce F Liu, Joyce Liu, Ju Liu, Ju-Fang Liu, Juan Liu, Juanjuan Liu, Juanxi Liu, Jue Liu, Jui-Tung Liu, Jun Liu, Jun O Liu, Jun Ting Liu, Jun Yi Liu, Jun-Jen Liu, Jun-Yan Liu, Jun-Yi Liu, Junbao Liu, Junchao Liu, Junfen Liu, Junhui Liu, Junjiang Liu, Junjie Liu, Junjin Liu, Junjun Liu, Junlin Liu, Junling Liu, Junnian Liu, Junpeng Liu, Junqi Liu, Junrong Liu, Juntao Liu, Juntian Liu, Junwen Liu, Junwu Liu, Junxi Liu, Junyan Liu, Junye Liu, Junying Liu, Junyu Liu, Juyao Liu, Kai Liu, Kai-Zheng Liu, Kaidong Liu, Kaijing Liu, Kaikun Liu, Kaiqi Liu, Kaisheng Liu, Kaitai Liu, Kaiwen Liu, Kang Liu, Kang-le Liu, Kangdong Liu, Kangwei Liu, Kathleen D Liu, Ke Liu, Ke-Tong Liu, Kechun Liu, Kehui Liu, Kejia Liu, Keng-Hau Liu, Keqiang Liu, Kexin Liu, Kiang Liu, Kuangyi Liu, Kun Liu, Kun-Cheng Liu, Kwei-Yan Liu, L L Liu, L Liu, L W Liu, Lan Liu, Lan-Xiang Liu, Lang Liu, Lanhao Liu, Le Liu, Lebin Liu, Lei Liu, Lele Liu, Leping Liu, Li Liu, Li-Fang Liu, Li-Min Liu, Li-Rong Liu, Li-Wen Liu, Li-Xuan Liu, Li-Ying Liu, Li-ping Liu, Lian Liu, Lianfei Liu, Liang Liu, Liang-Chen Liu, Liang-Feng Liu, Liangguo Liu, Liangji Liu, Liangjia Liu, Liangliang Liu, Liangyu Liu, Lianxin Liu, Lianyong Liu, Libin Liu, Lichao Liu, Lichun Liu, Lidong Liu, Liegang Liu, Lifang Liu, Ligang Liu, Lihua Liu, Lijuan Liu, Lijun Liu, Lili Liu, Liling Liu, Limin Liu, Liming Liu, Lin Liu, Lina Liu, Ling Liu, Ling-Yun Liu, Ling-Zhi Liu, Lingfei Liu, Lingjiao Liu, Lingjuan Liu, Linglong Liu, Lingyan Liu, Lining Liu, Linlin Liu, Linqing Liu, Linwen Liu, Liping Liu, Liqing Liu, Liqiong Liu, Liqun Liu, Lirong Liu, Liru Liu, Liu Liu, Liumei Liu, Liusheng Liu, Liwen Liu, Lixia Liu, Lixian Liu, Lixiao Liu, Liying Liu, Liyue Liu, Lizhen Liu, Long Liu, Longfei Liu, Longjian Liu, Longqian Liu, Longyang Liu, Longzhou Liu, Lu Liu, Luhong Liu, Lulu Liu, Luming Liu, Lunxu Liu, Luping Liu, Lushan Liu, Lv Liu, M L Liu, M Liu, Man Liu, Man-Ru Liu, Manjiao Liu, Manqi Liu, Manran Liu, Maolin Liu, Mei Liu, Mei-mei Liu, Meicen Liu, Meifang Liu, Meijiao Liu, Meijing Liu, Meijuan Liu, Meijun Liu, Meiling Liu, Meimei Liu, Meixin Liu, Meiyan Liu, Meng Han Liu, Meng Liu, Meng-Hui Liu, Meng-Meng Liu, Meng-Yue Liu, Mengduan Liu, Mengfan Liu, Mengfei Liu, Menggang Liu, Menghan Liu, Menghua Liu, Menghui Liu, Mengjia Liu, Mengjiao Liu, Mengke Liu, Menglin Liu, Mengling Liu, Mengmei Liu, Mengqi Liu, Mengqian Liu, Mengxi Liu, Mengxue Liu, Mengyang Liu, Mengying Liu, Mengyu Liu, Mengyuan Liu, Mengzhen Liu, Mi Liu, Mi-Hua Liu, Mi-Min Liu, Miao Liu, Miaoliang Liu, Min Liu, Minda Liu, Minetta C Liu, Ming Liu, Ming-Jiang Liu, Ming-Qi Liu, Mingcheng Liu, Mingchun Liu, Mingfan Liu, Minghui Liu, Mingjiang Liu, Mingjing Liu, Mingjun Liu, Mingli Liu, Mingming Liu, Mingna Liu, Mingqin Liu, Mingrui Liu, Mingsen Liu, Mingsong Liu, Mingxiao Liu, Mingxing Liu, Mingxu Liu, Mingyang Liu, Mingyao Liu, Mingying Liu, Mingyu Liu, Minhao Liu, Minxia Liu, Mo-Nan Liu, Modan Liu, Mouze Liu, Muqiu Liu, Musang Liu, N A Liu, N Liu, Na Liu, Na-Nv Liu, Na-Wei Liu, Nai-feng Liu, Naihua Liu, Naili Liu, Nan Liu, Nan-Song Liu, Nana Liu, Nannan Liu, Nanxi Liu, Ni Liu, Nian Liu, Ning Liu, Ning'ang Liu, Ningning Liu, Niya Liu, Ou Liu, Ouxuan Liu, P C Liu, Pan Liu, Panhong Liu, Panting Liu, Paul Liu, Pei Liu, Pei-Ning Liu, Peijian Liu, Peijie Liu, Peijun Liu, Peilong Liu, Peiqi Liu, Peiqing Liu, Peiwei Liu, Peixi Liu, Peiyao Liu, Peizhong Liu, Peng Liu, Pengcheng Liu, Pengfei Liu, Penghong Liu, Pengli Liu, Pengtao Liu, Pengyu Liu, Pengyuan Liu, Pentao Liu, Peter S Liu, Piaopiao Liu, Pinduo Liu, Ping Liu, Ping-Yen Liu, Pinghuai Liu, Pingping Liu, Pingsheng Liu, Q Liu, Qi Liu, Qi-Xian Liu, Qian Liu, Qian-Wen Liu, Qiang Liu, Qiang-Yuan Liu, Qiangyun Liu, Qianjin Liu, Qianqi Liu, Qianshuo Liu, Qianwei Liu, Qiao-Hong Liu, Qiaofeng Liu, Qiaoyan Liu, Qiaozhen Liu, Qiji Liu, Qiming Liu, Qin Liu, Qinfang Liu, Qing Liu, Qing-Huai Liu, Qing-Rong Liu, Qingbin Liu, Qingbo Liu, Qingguang Liu, Qingguo Liu, Qinghao Liu, Qinghong Liu, Qinghua Liu, Qinghuai Liu, Qinghuan Liu, Qinglei Liu, Qingping Liu, Qingqing Liu, Qingquan Liu, Qingsong Liu, Qingxia Liu, Qingxiang Liu, Qingyang Liu, Qingyou Liu, Qingyun Liu, Qingzhuo Liu, Qinqin Liu, Qiong Liu, Qiu-Ping Liu, Qiulei Liu, Qiuli Liu, Qiulu Liu, Qiushi Liu, Qiuxu Liu, Qiuyu Liu, Qiuyue Liu, Qiwei Liu, Qiyao Liu, Qiye Liu, Qizhan Liu, Quan Liu, Quan-Jun Liu, Quanxin Liu, Quanying Liu, Quanzhong Liu, Quentin Liu, Qun Liu, Qunlong Liu, Qunpeng Liu, R F Liu, R Liu, R Y Liu, Ran Liu, Rangru Liu, Ranran Liu, Ren Liu, Renling Liu, Ri Liu, Rong Liu, Rong-Zong Liu, Rongfei Liu, Ronghua Liu, Rongxia Liu, Rongxun Liu, Rui Liu, Rui-Jie Liu, Rui-Tian Liu, Rui-Xuan Liu, Ruichen Liu, Ruihua Liu, Ruijie Liu, Ruijuan Liu, Ruilong Liu, Ruiping Liu, Ruiqi Liu, Ruitong Liu, Ruixia Liu, Ruiyi Liu, Ruizao Liu, Runjia Liu, Runjie Liu, Runni Liu, Runping Liu, Ruochen Liu, Ruotian Liu, Ruowen Liu, Ruoyang Liu, Ruyi Liu, Ruyue Liu, S Liu, Saiji Liu, Sasa Liu, Sen Liu, Senchen Liu, Senqi Liu, Sha Liu, Shan Liu, Shan-Shan Liu, Shandong Liu, Shang-Feng Liu, Shang-Xin Liu, Shangjing Liu, Shangxin Liu, Shangyu Liu, Shangyuan Liu, Shangyun Liu, Shanhui Liu, Shanling Liu, Shanshan Liu, Shao-Bin Liu, Shao-Jun Liu, Shao-Yuan Liu, Shaobo Liu, Shaocheng Liu, Shaohua Liu, Shaojun Liu, Shaoqing Liu, Shaowei Liu, Shaoying Liu, Shaoyou Liu, Shaoyu Liu, Shaozhen Liu, Shasha Liu, Sheng Liu, Shengbin Liu, Shengjun Liu, Shengnan Liu, Shengyang Liu, Shengzhi Liu, Shengzhuo Liu, Shenhai Liu, Shenping Liu, Shi Liu, Shi-Lian Liu, Shi-Wei Liu, Shi-Yong Liu, Shi-guo Liu, ShiWei Liu, Shih-Ping Liu, Shijia Liu, Shijian Liu, Shijie Liu, Shijun Liu, Shikai Liu, Shikun Liu, Shilin Liu, Shing-Hwa Liu, Shiping Liu, Shiqian Liu, Shiquan Liu, Shiru Liu, Shixi Liu, Shiyan Liu, Shiyang Liu, Shiying Liu, Shiyu Liu, Shiyuan Liu, Shou-Sheng Liu, Shouguo Liu, Shoupei Liu, Shouxin Liu, Shouyang Liu, Shu Liu, Shu-Chen Liu, Shu-Jing Liu, Shu-Lin Liu, Shu-Qiang Liu, Shu-Qin Liu, Shuai Liu, Shuaishuai Liu, Shuang Liu, Shuangli Liu, Shuangzhu Liu, Shuhong Liu, Shuhua Liu, Shui-Bing Liu, Shujie Liu, Shujing Liu, Shujun Liu, Shulin Liu, Shuling Liu, Shumin Liu, Shun-Mei Liu, Shunfang Liu, Shuning Liu, Shunming Liu, Shuqian Liu, Shuqing Liu, Shuwen Liu, Shuxi Liu, Shuxian Liu, Shuya Liu, Shuyan Liu, Shuyu Liu, Si-Jin Liu, Si-Xu Liu, Si-Yan Liu, Si-jun Liu, Sicheng Liu, Sidan Liu, Side Liu, Sihao Liu, Sijing Liu, Sijun Liu, Silvia Liu, Simin Liu, Sipu Liu, Siqi Liu, Siqin Liu, Siru Liu, Sirui Liu, Sitian Liu, Siwen Liu, Sixi Liu, Sixin Liu, Sixiu Liu, Sixu Liu, Siyao Liu, Siyi Liu, Siyu Liu, Siyuan Liu, Song Liu, Song-Fang Liu, Song-Mei Liu, Song-Ping Liu, Songfang Liu, Songhui Liu, Songqin Liu, Songsong Liu, Songyi Liu, Su Liu, Su-Yun Liu, Sudong Liu, Suhuan Liu, Sui-Feng Liu, Suling Liu, Suosi Liu, Sushuang Liu, Susu Liu, Szu-Heng Liu, T H Liu, T Liu, Ta-Chih Liu, Taihang Liu, Taixiang Liu, Tang Liu, Tao Liu, Taoli Liu, Taotao Liu, Te Liu, Teng Liu, Tengfei Liu, Tengli Liu, Teresa T Liu, Tian Liu, Tian Shu Liu, Tianhao Liu, Tianhu Liu, Tianjia Liu, Tianjiao Liu, Tianlai Liu, Tianlang Liu, Tianlong Liu, Tianqiang Liu, Tianrui Liu, Tianshu Liu, Tiantian Liu, Tianyao Liu, Tianyi Liu, Tianyu Liu, Tianze Liu, Tiemin Liu, Tina Liu, Ting Liu, Ting-Li Liu, Ting-Ting Liu, Ting-Yuan Liu, Tingjiao Liu, Tingting Liu, Tong Liu, Tonglin Liu, Tongtong Liu, Tongyan Liu, Tongyu Liu, Tongyun Liu, Tongzheng Liu, Tsang-Wu Liu, Tsung-Yun Liu, Vincent W S Liu, W Liu, W-Y Liu, Wan Liu, Wan-Chun Liu, Wan-Di Liu, Wan-Guo Liu, Wan-Ying Liu, Wang Liu, Wangrui Liu, Wanguo Liu, Wangyang Liu, Wanjun Liu, Wanli Liu, Wanlu Liu, Wanqi Liu, Wanqing Liu, Wanting Liu, Wei Liu, Wei-Chieh Liu, Wei-Hsuan Liu, Wei-Hua Liu, Weida Liu, Weifang Liu, Weifeng Liu, Weiguo Liu, Weihai Liu, Weihong Liu, Weijian Liu, Weijie Liu, Weijun Liu, Weilin Liu, Weimin Liu, Weiming Liu, Weina Liu, Weiqin Liu, Weiqing Liu, Weiren Liu, Weisheng Liu, Weishuo Liu, Weiwei Liu, Weiyang Liu, Wen Liu, Wen Yuan Liu, Wen-Chun Liu, Wen-Di Liu, Wen-Fang Liu, Wen-Jie Liu, Wen-Jing Liu, Wen-Qiang Liu, Wen-Tao Liu, Wen-ling Liu, Wenbang Liu, Wenbin Liu, Wenbo Liu, Wenchao Liu, Wenen Liu, Wenfeng Liu, Wenhan Liu, Wenhao Liu, Wenhua Liu, Wenjie Liu, Wenjing Liu, Wenlang Liu, Wenli Liu, Wenling Liu, Wenlong Liu, Wenna Liu, Wenping Liu, Wenqi Liu, Wenrui Liu, Wensheng Liu, Wentao Liu, Wenwu Liu, Wenxiang Liu, Wenxuan Liu, Wenya Liu, Wenyan Liu, Wenyi Liu, Wenzhong Liu, Wu Liu, Wuping Liu, Wuyang Liu, X C Liu, X Liu, X P Liu, X-D Liu, Xi Liu, Xi-Yu Liu, Xia Liu, Xia-Meng Liu, Xialin Liu, Xian Liu, Xianbao Liu, Xianchen Liu, Xianda Liu, Xiang Liu, Xiang-Qian Liu, Xiang-Yu Liu, Xiangchen Liu, Xiangfei Liu, Xianglan Liu, Xiangli Liu, Xiangliang Liu, Xianglu Liu, Xiangning Liu, Xiangping Liu, Xiangsheng Liu, Xiangtao Liu, Xiangting Liu, Xiangxiang Liu, Xiangxuan Liu, Xiangyong Liu, Xiangyu Liu, Xiangyun Liu, Xianli Liu, Xianling Liu, Xiansheng Liu, Xianyang Liu, Xiao Dong Liu, Xiao Liu, Xiao Yan Liu, Xiao-Cheng Liu, Xiao-Dan Liu, Xiao-Gang Liu, Xiao-Guang Liu, Xiao-Huan Liu, Xiao-Jiao Liu, Xiao-Li Liu, Xiao-Ling Liu, Xiao-Ning Liu, Xiao-Qiu Liu, Xiao-Qun Liu, Xiao-Rong Liu, Xiao-Song Liu, Xiao-Xiao Liu, Xiao-lan Liu, Xiaoan Liu, Xiaobai Liu, Xiaobei Liu, Xiaobing Liu, Xiaocen Liu, Xiaochuan Liu, Xiaocong Liu, Xiaodan Liu, Xiaoding Liu, Xiaodong Liu, Xiaofan Liu, Xiaofang Liu, Xiaofei Liu, Xiaogang Liu, Xiaoguang Liu, Xiaoguang Margaret Liu, Xiaohan Liu, Xiaoheng Liu, Xiaohong Liu, Xiaohua Liu, Xiaohuan Liu, Xiaohui Liu, Xiaojie Liu, Xiaojing Liu, Xiaoju Liu, Xiaojun Liu, Xiaole Shirley Liu, Xiaolei Liu, Xiaoli Liu, Xiaolin Liu, Xiaoling Liu, Xiaoman Liu, Xiaomei Liu, Xiaomeng Liu, Xiaomin Liu, Xiaoming Liu, Xiaona Liu, Xiaonan Liu, Xiaopeng Liu, Xiaoping Liu, Xiaoqian Liu, Xiaoqiang Liu, Xiaoqin Liu, Xiaoqing Liu, Xiaoran Liu, Xiaosong Liu, Xiaotian Liu, Xiaoting Liu, Xiaowei Liu, Xiaoxi Liu, Xiaoxia Liu, Xiaoxiao Liu, Xiaoxu Liu, Xiaoxue Liu, Xiaoya Liu, Xiaoyan Liu, Xiaoyang Liu, Xiaoye Liu, Xiaoying Liu, Xiaoyong Liu, Xiaoyu Liu, Xiawen Liu, Xibao Liu, Xibing Liu, Xie-hong Liu, Xiehe Liu, Xiguang Liu, Xijun Liu, Xili Liu, Xin Liu, Xin-Hua Liu, Xin-Yan Liu, Xinbo Liu, Xinchang Liu, Xing Liu, Xing-De Liu, Xing-Li Liu, Xing-Yang Liu, Xingbang Liu, Xingde Liu, Xinghua Liu, Xinghui Liu, Xingjing Liu, Xinglei Liu, Xingli Liu, Xinglong Liu, Xinguo Liu, Xingxiang Liu, Xingyi Liu, Xingyu Liu, Xinhua Liu, Xinjun Liu, Xinlei Liu, Xinli Liu, Xinmei Liu, Xinmin Liu, Xinran Liu, Xinru Liu, Xinrui Liu, Xintong Liu, Xinxin Liu, Xinyao Liu, Xinyi Liu, Xinying Liu, Xinyong Liu, Xinyu Liu, Xinyue Liu, Xiong Liu, Xiqiang Liu, Xiru Liu, Xishan Liu, Xiu Liu, Xiufen Liu, Xiufeng Liu, Xiuheng Liu, Xiuling Liu, Xiumei Liu, Xiuqin Liu, Xiyong Liu, Xu Liu, Xu-Dong Liu, Xu-Hui Liu, Xuan Liu, Xuanlin Liu, Xuanyu Liu, Xuanzhu Liu, Xue Liu, Xue-Lian Liu, Xue-Min Liu, Xue-Qing Liu, Xue-Zheng Liu, Xuefang Liu, Xuejing Liu, Xuekui Liu, Xuelan Liu, Xueling Liu, Xuemei Liu, Xuemeng Liu, Xuemin Liu, Xueping Liu, Xueqin Liu, Xueqing Liu, Xueru Liu, Xuesen Liu, Xueshibojie Liu, Xuesong Liu, Xueting Liu, Xuewei Liu, Xuewen Liu, Xuexiu Liu, Xueying Liu, Xueyuan Liu, Xuezhen Liu, Xuezheng Liu, Xuezhi Liu, Xufeng Liu, Xuguang Liu, Xujie Liu, Xulin Liu, Xuming Liu, Xunhua Liu, Xunyue Liu, Xuxia Liu, Xuxu Liu, Xuyi Liu, Xuying Liu, Y H Liu, Y L Liu, Y Liu, Y Y Liu, Ya Liu, Ya-Jin Liu, Ya-Kun Liu, Ya-Wei Liu, Yadong Liu, Yafei Liu, Yajing Liu, Yajuan Liu, Yaling Liu, Yalu Liu, Yan Liu, Yan-Li Liu, Yanan Liu, Yanchao Liu, Yanchen Liu, Yandong Liu, Yanfei Liu, Yanfen Liu, Yanfeng Liu, Yang Liu, Yange Liu, Yangfan Liu, Yangfan P Liu, Yangjun Liu, Yangkai Liu, Yangruiyu Liu, Yangyang Liu, Yanhong Liu, Yanhua Liu, Yanhui Liu, Yanjie Liu, Yanju Liu, Yanjun Liu, Yankuo Liu, Yanli Liu, Yanliang Liu, Yanling Liu, Yanman Liu, Yanmin Liu, Yanping Liu, Yanqing Liu, Yanqiu Liu, Yanquan Liu, Yanru Liu, Yansheng Liu, Yansong Liu, Yanting Liu, Yanwu Liu, Yanxiao Liu, Yanyan Liu, Yanyao Liu, Yanying Liu, Yanyun Liu, Yao Liu, Yao-Hui Liu, Yaobo Liu, Yaoquan Liu, Yaou Liu, Yaowen Liu, Yaoyao Liu, Yaozhong Liu, Yaping Liu, Yaqiong Liu, Yarong Liu, Yaru Liu, Yating Liu, Yaxin Liu, Ye Liu, Ye-Dan Liu, Yehai Liu, Yen-Chen Liu, Yen-Chun Liu, Yen-Nien Liu, Yeqing Liu, Yi Liu, Yi-Chang Liu, Yi-Chien Liu, Yi-Han Liu, Yi-Hung Liu, Yi-Jia Liu, Yi-Ling Liu, Yi-Meng Liu, Yi-Ming Liu, Yi-Yun Liu, Yi-Zhang Liu, YiRan Liu, Yibin Liu, Yibing Liu, Yicun Liu, Yidan Liu, Yidong Liu, Yifan Liu, Yifu Liu, Yihao Liu, Yiheng Liu, Yihui Liu, Yijing Liu, Yilei Liu, Yili Liu, Yilin Liu, Yimei Liu, Yiming Liu, Yin Liu, Yin-Ping Liu, Yinchu Liu, Yinfang Liu, Ying Liu, Ying Poi Liu, Yingchun Liu, Yinghua Liu, Yinghuan Liu, Yinghui Liu, Yingjun Liu, Yingli Liu, Yingwei Liu, Yingxia Liu, Yingyan Liu, Yingyi Liu, Yingying Liu, Yingzi Liu, Yinhe Liu, Yinhui Liu, Yining Liu, Yinjiang Liu, Yinping Liu, Yinuo Liu, Yiping Liu, Yiqing Liu, Yitian Liu, Yiting Liu, Yitong Liu, Yiwei Liu, Yiwen Liu, Yixiang Liu, Yixiao Liu, Yixuan Liu, Yiyang Liu, Yiyi Liu, Yiyuan Liu, Yiyun Liu, Yizhi Liu, Yizhuo Liu, Yong Liu, Yong Mei Liu, Yong-Chao Liu, Yong-Hong Liu, Yong-Jian Liu, Yong-Jun Liu, Yong-Tai Liu, Yong-da Liu, Yongchao Liu, Yonggang Liu, Yonggao Liu, Yonghong Liu, Yonghua Liu, Yongjian Liu, Yongjie Liu, Yongjun Liu, Yongli Liu, Yongmei Liu, Yongming Liu, Yongqiang Liu, Yongshuo Liu, Yongtai Liu, Yongtao Liu, Yongtong Liu, Yongxiao Liu, Yongyue Liu, You Liu, You-ping Liu, Youan Liu, Youbin Liu, Youdong Liu, Youhan Liu, Youlian Liu, Youwen Liu, Yu Liu, Yu Xuan Liu, Yu-Chen Liu, Yu-Ching Liu, Yu-Hui Liu, Yu-Li Liu, Yu-Lin Liu, Yu-Peng Liu, Yu-Wei Liu, Yu-Zhang Liu, YuHeng Liu, Yuan Liu, Yuan-Bo Liu, Yuan-Jie Liu, Yuan-Tao Liu, YuanHua Liu, Yuanchu Liu, Yuanfa Liu, Yuanhang Liu, Yuanhui Liu, Yuanjia Liu, Yuanjiao Liu, Yuanjun Liu, Yuanliang Liu, Yuantao Liu, Yuantong Liu, Yuanxiang Liu, Yuanxin Liu, Yuanxing Liu, Yuanying Liu, Yuanyuan Liu, Yubin Liu, Yuchen Liu, Yue Liu, Yuecheng Liu, Yuefang Liu, Yuehong Liu, Yueli Liu, Yueping Liu, Yuetong Liu, Yuexi Liu, Yuexin Liu, Yuexing Liu, Yueyang Liu, Yueyun Liu, Yufan Liu, Yufei Liu, Yufeng Liu, Yuhao Liu, Yuhe Liu, Yujia Liu, Yujiang Liu, Yujie Liu, Yujun Liu, Yulan Liu, Yuling Liu, Yulong Liu, Yumei Liu, Yumiao Liu, Yun Liu, Yun-Cai Liu, Yun-Qiang Liu, Yun-Ru Liu, Yun-Zi Liu, Yunfen Liu, Yunfeng Liu, Yuning Liu, Yunjie Liu, Yunlong Liu, Yunqi Liu, Yunqiang Liu, Yuntao Liu, Yunuan Liu, Yunuo Liu, Yunxia Liu, Yunyun Liu, Yuping Liu, Yupu Liu, Yuqi Liu, Yuqiang Liu, Yuqing Liu, Yurong Liu, Yuru Liu, Yusen Liu, Yutao Liu, Yutian Liu, Yuting Liu, Yutong Liu, Yuwei Liu, Yuxi Liu, Yuxia Liu, Yuxiang Liu, Yuxin Liu, Yuxuan Liu, Yuyan Liu, Yuyi Liu, Yuyu Liu, Yuyuan Liu, Yuzhen Liu, Yv-Xuan Liu, Z H Liu, Z Q Liu, Z Z Liu, Zaiqiang Liu, Zan Liu, Zaoqu Liu, Ze Liu, Zefeng Liu, Zekun Liu, Zeming Liu, Zengfu Liu, Zeyu Liu, Zezhou Liu, Zhangyu Liu, Zhangyuan Liu, Zhansheng Liu, Zhao Liu, Zhaoguo Liu, Zhaoli Liu, Zhaorui Liu, Zhaotian Liu, Zhaoxiang Liu, Zhaoxun Liu, Zhaoyang Liu, Zhe Liu, Zhekai Liu, Zheliang Liu, Zhen Liu, Zhen-Lin Liu, Zhendong Liu, Zhenfang Liu, Zhenfeng Liu, Zheng Liu, Zheng-Hong Liu, Zheng-Yu Liu, ZhengYi Liu, Zhengbing Liu, Zhengchuang Liu, Zhengdong Liu, Zhenghao Liu, Zhengkun Liu, Zhengtang Liu, Zhengting Liu, Zhenguo Liu, Zhengxia Liu, Zhengye Liu, Zhenhai Liu, Zhenhao Liu, Zhenhua Liu, Zhenjiang Liu, Zhenjiao Liu, Zhenjie Liu, Zhenkui Liu, Zhenlei Liu, Zhenmi Liu, Zhenming Liu, Zhenna Liu, Zhenqian Liu, Zhenqiu Liu, Zhenwei Liu, Zhenxing Liu, Zhenxiu Liu, Zhenzhen Liu, Zhenzhu Liu, Zhi Liu, Zhi Y Liu, Zhi-Fen Liu, Zhi-Guo Liu, Zhi-Jie Liu, Zhi-Kai Liu, Zhi-Ping Liu, Zhi-Ren Liu, Zhi-Wen Liu, Zhi-Ying Liu, Zhicheng Liu, Zhifang Liu, Zhigang Liu, Zhiguo Liu, Zhihan Liu, Zhihao Liu, Zhihong Liu, Zhihua Liu, Zhihui Liu, Zhijia Liu, Zhijie Liu, Zhikui Liu, Zhili Liu, Zhiming Liu, Zhipeng Liu, Zhiping Liu, Zhiqian Liu, Zhiqiang Liu, Zhiru Liu, Zhirui Liu, Zhishuo Liu, Zhitao Liu, Zhiteng Liu, Zhiwei Liu, Zhixiang Liu, Zhixue Liu, Zhiyan Liu, Zhiying Liu, Zhiyong Liu, Zhiyuan Liu, Zhong Liu, Zhong Wu Liu, Zhong-Hua Liu, Zhong-Min Liu, Zhong-Qiu Liu, Zhong-Wu Liu, Zhong-Ying Liu, Zhongchun Liu, Zhongguo Liu, Zhonghua Liu, Zhongjian Liu, Zhongjuan Liu, Zhongmin Liu, Zhongqi Liu, Zhongqiu Liu, Zhongwei Liu, Zhongyu Liu, Zhongyue Liu, Zhongzhong Liu, Zhou Liu, Zhou-di Liu, Zhu Liu, Zhuangjun Liu, Zhuanhua Liu, Zhuo Liu, Zhuoyuan Liu, Zi Hao Liu, Zi-Hao Liu, Zi-Lun Liu, Zi-Ye Liu, Zi-wen Liu, Zichuan Liu, Zihang Liu, Zihao Liu, Zihe Liu, Ziheng Liu, Zijia Liu, Zijian Liu, Zijing J Liu, Zimeng Liu, Ziqian Liu, Ziqin Liu, Ziteng Liu, Zitian Liu, Ziwei Liu, Zixi Liu, Zixuan Liu, Ziyang Liu, Ziying Liu, Ziyou Liu, Ziyuan Liu, Ziyue Liu, Zong-Chao Liu, Zong-Yuan Liu, Zonghua Liu, Zongjun Liu, Zongtao Liu, Zongxiang Liu, Zu-Guo Liu, Zuguo Liu, Zuohua Liu, Zuojin Liu, Zuolu Liu, Zuyi Liu, Zuyun Liu
articles

Loss of Nup160 dysregulates Cdc42 in the podocytes of podocyte-specific Nup160 knockout mice.

Deying Liu, Jiaxin Li, Chan Xu +7 more · 2025 · Human molecular genetics · Oxford University Press · added 2026-04-24
Mutations in four genes encoding the outer ring complex of nuclear pore complexes (NPCs), NUP85, NUP107, NUP133 and NUP160, cause monogenic steroid-resistant nephrotic syndrome (SRNS). Knockout of NUP Show more
Mutations in four genes encoding the outer ring complex of nuclear pore complexes (NPCs), NUP85, NUP107, NUP133 and NUP160, cause monogenic steroid-resistant nephrotic syndrome (SRNS). Knockout of NUP85, NUP107, or NUP133 in immortalized human podocytes activates CDC42, an important effector of SRNS pathogenesis. However, it is unknown whether or not loss of NUP160 dysregulates CDC42 in the podocytes. Here, we generated a podocyte-specific Nup160 knockout mouse model with double-fluorescent (mT/mG) Cre reporter genes using CRISPR/Cas9 and Cre/loxP technologies. We investigated nephrotic syndrome-associated phenotypes in the Nup160podo-/- mice, and performed single-cell transcriptomic and proteomic analysis of glomerular suspension cells and cultured primary podocytes, respectively. The Nup160podo-/- mice exhibited progressive proteinuria and fusion of podocyte foot processes. We found decreased Cdc42 protein and normal Cdc42 transcriptional level in the podocytes of the Nup160podo-/- mice using analysis of single-cell transcriptomes and proteomes. We subsequently observed that Cdc42 protein decreased in both kidney tissues and cultured primary podocytes of the Nup160podo-/- mice, although Cdc42 mRNA levels were elevated in the cultured primary podocytes of the Nup160podo-/- mice. We also found that Cdc42 activity was significantly reduced in the cultured primary podocytes of the Nup160podo-/- mice. In conclusion, loss of Nup160 dysregulated Cdc42 in the podocytes of the Nup160podo-/- mice with proteinuria and fusion of podocyte foot processes. Our findings suggest that the dysregulation of CDC42 may contribute to the pathogenesis of SRNS in patients with mutations in NUP160. Show less
no PDF DOI: 10.1093/hmg/ddaf064
NUP160

Bayesian fine-mapping and Mendelian randomization leveraging expression quantitative trait loci reveal novel candidate causal genes for body conformation traits in cattle.

Jun Teng, Chongwei Duan, Xinyi Zhang +9 more · 2025 · Journal of dairy science · added 2026-04-24
Cattle body size measurements constitute the conformation traits that facilitate their production, fertility, and longevity status. Prioritizing functional variants and causal genes of conformation tr Show more
Cattle body size measurements constitute the conformation traits that facilitate their production, fertility, and longevity status. Prioritizing functional variants and causal genes of conformation traits is essential for understanding their genetic basis. In this study, we conducted single-trait and multitrait GWAS for 20 body conformation traits using imputed sequence data in 7,674 Chinese Holstein individuals and identified 27 QTL regions. Leveraging these QTL regions, we performed multitrait Bayesian fine-mapping to identify 30 independent credible sets of putative causal variants. Incorporating GWAS and cis-acting expression QTL data, Mendelian randomization was used to infer 153 putative causal gene-trait relationships. The previously reported genes, such as CCND2, TMTC2, and NRG3, were confirmed in our study. Of note, several novel candidate causal genes were also identified, such as C1R, RIMS1, SERPINB8, NETO2, TTYH3, TTC3, ANAPC4, and PSMD13. Our results provide new insights into the regulatory mechanisms of body conformation traits in cattle. Show less
no PDF DOI: 10.3168/jds.2025-26361
ANAPC4

Impact of an extended light regimen imposed during nursery period on the performance and lipid metabolism of weanling pigs.

Guangfan Liu, Fen Su, Xingyue Zou +2 more · 2025 · Animal bioscience · added 2026-04-24
This study aimed to assess the impact of a prolonged photoperiod on the growth performance and lipid metabolism of weaned piglets. Twenty-four piglets weaned at 28 days of age were randomly dichotomiz Show more
This study aimed to assess the impact of a prolonged photoperiod on the growth performance and lipid metabolism of weaned piglets. Twenty-four piglets weaned at 28 days of age were randomly dichotomized into two groups that were alternatively subjected to either long photoperiod (LP) group (16 L:8 D) or short photoperiod (SP) group (10 L:14 D) for 42days. Four replicates of three animals per replicates were used per experimental treatment. Our results demonstrated that prolonged photoperiod increased piglet body weight, average daily weight gain (ADG), backfat thickness (BF), backfat index during the nursery period, and increased ADG, average daily feed intake (ADFI), and decreased the F/G of piglets during the experiment days 29 to 42. Meanwhile, we observed LP piglets' plasma melatonin, growth hormone and serotonin levels were decreased at 14 d and 42 d compared to SP piglets. Moreover, up-regulated mRNA or protein expression of PPARγ and CEBPα, and lower mRNA or protein expression of MTR1, ATGL, HSL, PPARα, and CPT1α, were observed in back subcutaneous fat of LP group compared with that of SP group. Significant increases were observed in the mRNA or protein contents of lipogenic genes, including C/EBPα, SREBP-1c, ACCα, and FAS, in the liver of LP piglets, whereas CPT1α and ACOX1 mRNA levels and PPARα and MTR1 protein expression were significantly downregulated in LP group compared to SP group. Extended photoperiod also increased lipid content in longissimus dorsi muscle that was associated with higher mRNA or protein levels of SREBP-1c, ACCα, FAS, Pref1, and LPL, decreased mRNA or protein contents of LeptinR, MTR1, HSL, and ACOX1. Together, these findings suggest that there is an advantage, in terms of growth performance and fat deposition, in imposing a prolonged light program (16-h light/d) on nursery piglets to alleviate the negative aspects of weaning stress. Show less
📄 PDF DOI: 10.5713/ab.24.0270
LPL

High-density lipoprotein cholesterol and nuclear factor I A in type 2 diabetes and mild cognitive impairment: biomarkers and mechanistic insights.

Jin Zhao, Tingying Zhang, Yunling Zhu +5 more · 2025 · Archives of medical science : AMS · added 2026-04-24
Type 2 diabetes (T2D) and mild cognitive impairment (MCI) are interrelated conditions that significantly impair quality of life. This study aimed to identify a feasible biomarker for assessing T2D-MCI Show more
Type 2 diabetes (T2D) and mild cognitive impairment (MCI) are interrelated conditions that significantly impair quality of life. This study aimed to identify a feasible biomarker for assessing T2D-MCI risk and to evaluate a potential therapeutic strategy. We integrated data from the National Health and Nutrition Examination Survey (NHANES) with Mendelian randomization (MR) to investigate genetic causal relationships between T2D, MCI, and their shared biomarkers. Transcriptomic analysis identified T2D-associated genes. Clinical trials evaluated the short-term effects of modified fasting therapy (MFT) on glucose regulation and cognitive function. Cellular assays and patient samples were used to validate the regulatory roles of key genes in biochemical markers and downstream signaling pathways. Among 6,356 T2D and 1,138 MCI subjects, vitamin D, high-density lipoprotein cholesterol (HDL-C), globulin, and creatinine were associated with both conditions. MR analysis showed that higher HDL-C levels reduced T2D risk (0.9059, 95% CI: 0.8666-0.9470) but increased MCI risk (OR = 1.0482, 95% CI: 1.0216-1.0755). Nuclear factor I A ( HDL-C has divergent genetic effects on T2D and MCI. Show less
📄 PDF DOI: 10.5114/aoms/208529
APOE

IL-27Ra promotes the progression of neonatal necrotizing enterocolitis.

Yuling Yang, Yingyan Liu, Limei Zhong +5 more · 2025 · Scientific reports · Nature · added 2026-04-24
Neonatal necrotizing enterocolitis (NEC) is a life-threatening gastrointestinal disease of premature infants, characterized by immune dysregulation and compromised intestinal barrier integrity. Interl Show more
Neonatal necrotizing enterocolitis (NEC) is a life-threatening gastrointestinal disease of premature infants, characterized by immune dysregulation and compromised intestinal barrier integrity. Interleukin-27 receptor α (IL-27Ra), a critical component of the JAK-STAT signaling pathway, exhibits dual pro- and anti-inflammatory roles in various inflammatory conditions. However, its role in NEC pathogenesis remains unclear. To elucidate the functional role of IL-27Ra in NEC development and assess its potential as a therapeutic target. A multi-tiered approach was employed, including integrative analysis of clinical NEC specimens by single-cell and bulk RNA sequencing, and a neonatal mouse NEC model. NEC was induced in mice via hyperosmolar formula feeding combined with LPS gavage, intermittent hypoxia, and cold stress. Additional experiments included immunofluorescence staining for IL-27Ra, cytokine profiling (ELISA, quantitative real-time PCR (qPCR)), use of IL-27Ra knockout (IL-27Ra Show less
📄 PDF DOI: 10.1038/s41598-025-26899-w
IL27

Polypropylene microplastics triggered mouse kidney lipidome reprogramming combined with ROS stress as revealed by lipidomics and Raman biospectra.

Mingying Liu, Miao Wang, Xinglin Sun +6 more · 2025 · Chemosphere · Elsevier · added 2026-04-24
Microplastics intrigue kidney toxicity such as mitochondrial dysfunction and inflammation promotion. However, as an organ relying heavily on fatty acid oxidation, how microplastics influence kidney li Show more
Microplastics intrigue kidney toxicity such as mitochondrial dysfunction and inflammation promotion. However, as an organ relying heavily on fatty acid oxidation, how microplastics influence kidney lipidomes remain unclear. Hence, we performed Raman spectra and multidimensional mass spectrometry-based shotgun lipidomics to decode kidney lipidomics landscape under polypropylene microplastics exposure. Kidney functions and cellular redox homeostasis were remarkably disturbed as revealed by levels of biochemical renal function markers, malonaldehyde, hydrogen peroxide and antioxidants. Ultrastructure alterations including the foot process fusion implied the kidney injury associated with lipidomic changes. Raman spectra successfully further confirmed the cellular change of reactive oxygen species and lipid disorders. Lipidomics showed that polypropylene microplastics caused abnormal lipidome and irregular exchange by remodeling triglycerides and phospholipids. Genes involved in lipid metabolism such as Fads1 and Elovl5 exhibited highly diversified expression profiles responding to polypropylene microplastics stress and possessed significant correlations with ROS indicators. These results explained ultrastructure alterations and aggravation of kidney injuries. Our work revealed polypropylene microplastics inducing lipidomic detriment in mouse kidney by Raman spectra and lipidomics firstly, elucidating the significances of lipidomic remodeling coupled with ROS stress in the kidney damages. The findings provided reliable evidence on the health risks of polypropylene microplastics in kidney. Show less
no PDF DOI: 10.1016/j.chemosphere.2024.143926
FADS1

Mendelian randomization and single-cell analysis reveal causal relationships between renal clear cell carcinoma, kidney fibrosis, and inflammatory factors.

Xian Chen, Hui Wang, Qianqian Li +4 more · 2025 · Discover oncology · Springer · added 2026-04-24
Renal clear cell carcinoma (RCC) is the most common type of kidney cancer, and its relationship with kidney fibrosis and inflammatory responses has attracted considerable attention. However, whether c Show more
Renal clear cell carcinoma (RCC) is the most common type of kidney cancer, and its relationship with kidney fibrosis and inflammatory responses has attracted considerable attention. However, whether causal relationships exist among these associations remains unclear, as traditional observational studies are susceptible to confounding factors. To evaluate causal relationships between kidney cancer, kidney fibrosis, and inflammatory factors using Mendelian randomization, and explore tumor microenvironment heterogeneity through single-cell analysis. Based on large-scale GWAS data, bidirectional Mendelian randomization analysis was performed to assess causal relationships between kidney cancer and kidney fibrosis, using MR Egger, inverse variance weighted (IVW), and weighted mode methods. Causal associations between kidney cancer and inflammatory factors including Axin-1, C-C motif chemokine 28, and interleukin-10 receptor subunit were analyzed. Single-cell RNA sequencing data from the GEO database (GSM4819725) was integrated for tumor microenvironment analysis. Bidirectional Mendelian randomization analysis revealed no significant causal relationship between kidney cancer and kidney fibrosis [kidney cancer→kidney fibrosis: IVW OR=0.992(95%CI: 0.913-1.077, P=0.842); kidney fibrosis→kidney cancer: IVW OR=0.922(95%CI: 0.824-1.030, P=0.151)]. However, significant positive causal associations were identified between kidney cancer and multiple inflammatory factors: Axin-1 levels [OR=1.448(95%CI: 1.107-1.894, P=0.007)], C-C motif chemokine 28 [OR=1.287(95%CI: 1.076-1.540, P=0.006)], and interleukin-10 receptor subunit [OR=1.135(95%CI: 1.032-1.248, P=0.009)]. Sensitivity analyses confirmed the robustness of results. Single-cell analysis revealed cellular heterogeneity in the tumor microenvironment, including various cell types such as immune cells, T cells, and NK cells, with pseudotime analysis demonstrating cell differentiation trajectories and dynamic gene expression changes. Mendelian randomization analysis provides genetic evidence for causal relationships between kidney cancer and inflammatory factors, while excluding direct causal associations between kidney cancer and kidney fibrosis. Show less
📄 PDF DOI: 10.1007/s12672-025-03343-z
AXIN1

Biomimetic non-collagenous proteins-calcium phosphate complex with superior osteogenesis via regulating macrophage IL-27 secretion.

Shenglong Tan, Xinghong Luo, Yifan Wang +9 more · 2025 · Biomaterials · Elsevier · added 2026-04-24
Traumatic defects or non-union fractures presents a substantial challenge in the fields of tissue engineering and regenerative medicine. Although synthetic calcium phosphate-based biomaterials (CaPs) Show more
Traumatic defects or non-union fractures presents a substantial challenge in the fields of tissue engineering and regenerative medicine. Although synthetic calcium phosphate-based biomaterials (CaPs) such as dibasic calcium phosphate anhydrate (DCPA) are commonly employed for bone repair, their inadequate cellular immune responses significantly impede sustained degradation and optimal osteogenesis. In this study, drawing inspiration from the key structure of an acidic non-collagenous protein-CaP complex (ANCPs-CaP) essential for natural bone formation, we prepared biomimetic mineralized dibasic calcium phosphate (MDCPA). This preparation utilized plant-derived non-collagenous protein Zein as the organic template and acidic artificial saliva as the mineralization medium. Physicochemical property analysis revealed that MDCPA is a complex of Zein and DCPA, which mimics the composite of the natural ANCP-CaP. Moreover, MDCPA exhibited enhanced biodegradability and osteogenic potential. Mechanistic insight revealed that MDCPA can be phagocytized and degraded by macrophages via the FCγRIII receptor, leading to the release of interleukin 27 (IL-27), which promotes osteogenic differentiation by osteoimmunomodulation. The critical role of IL-27 in osteogenesis is further confirmed using IL-27 gene knockout mice. Additionally, MDCPA demonstrates effective healing of critical-sized defects in rat cranial bones within only 4 w, providing a promising basis and valuable insights for critical-sized bone defects regeneration. Show less
no PDF DOI: 10.1016/j.biomaterials.2024.122917
IL27

Disruption of cerebral cholesterol homeostasis by PS-NPs: astrocytic endoplasmic reticulum stress.

Lei Tian, Yizhe Wei, Jianping Ma +10 more · 2025 · Journal of nanobiotechnology · BioMed Central · added 2026-04-24
Cholesterol plays a crucial role in regulating synaptic membrane fluidity and ion channels. Due to the blood-brain barrier, cholesterol in the brain is primarily self-synthesized by astrocytes. Howeve Show more
Cholesterol plays a crucial role in regulating synaptic membrane fluidity and ion channels. Due to the blood-brain barrier, cholesterol in the brain is primarily self-synthesized by astrocytes. However, limited research has been conducted on the effects of polystyrene nanoplastic (PS-NPs) on intracranial cholesterol metabolic pathways. In this study, we exposed whole-brain organoids (WBOs) to PS-NPs and identified significant changes in endoplasmic reticulum stress and cholesterol biosynthesis pathways through whole-transcriptome sequencing. To investigate potential mechanisms of altered cholesterol pathways, we constructed a Transwell neuronal-astrocyte co-culture model. Results demonstrated that PS-NPs induced significant endoplasmic reticulum stress in astrocytes, specifically manifested by elevated levels of ATF4 and CHOP, along with increased autophagy indicated by the elevated LC3-II/I ratio. PS-NPs significantly inhibited the AKT/ACLY pathway of cholesterol biosynthesis, leading to marked reductions in acetyl-CoA and cholesterol within astrocytes (P < 0.05). In addition, PS-NPs led to a significant reduction of apolipoprotein APOE, which hindered cholesterol transport and ultimately inhibited synaptin (SYN) formation. In summary, PS-NPs induce endoplasmic reticulum stress and autophagy in astrocytes, impair cholesterol de novo synthesis and apolipoprotein-mediated transport, ultimately inhibiting neuronal synaptogenesis. Furthermore, specific inhibition of ERs restored cholesterol synthesis in astrocytes and neuronal synapses. This study demonstrates that PS-NPs produce neurotoxic effects by affecting cholesterol homeostasis in the brain. Show less
📄 PDF DOI: 10.1186/s12951-025-03949-z
APOE

Branched-chain amino acid and cancer: metabolism, immune microenvironment and therapeutic targets.

Hao Xiong, Ruiqi Liu, Keke Xu +7 more · 2025 · Journal of translational medicine · BioMed Central · added 2026-04-24
Cancer is one of the major diseases threatening human health in the world. According to the latest global cancer statistics from the International Agency for Research on Cancer (IARC), there were appr Show more
Cancer is one of the major diseases threatening human health in the world. According to the latest global cancer statistics from the International Agency for Research on Cancer (IARC), there were approximately 20 million new cancer cases and 10 million cancer deaths worldwide. Amidst this global health concern, branched chain amino acids have emerged as key players, playing an important role in the occurrence and development of cancer. In certain malignancies like colorectal cancer, the average level of BCAA in tumor tissues is twice that in normal tissues. BCAA metabolism is intricately associated with the progression of multiple tumors and is modulated by diverse enzymes, including BCAT, BCKDH, and BCKDK. The metabolism of BCAA involves multiple enzymes and biochemical processes via signaling pathways such as PI3K/AKT/mTOR and AMPK/mTOR, etc. In addition, mTOR inhibitors show potential value in cancer treatment by regulating the metabolism and signaling pathways of tumor cells, which provides a new direction for anticancer efforts. Simultaneously, BCAAs are closely associated with tumor immunity, including NK cells, CD4 Show less
📄 PDF DOI: 10.1186/s12967-025-06664-3
BCKDK

Design of Anti-Tumor RNA Nanoparticles and Their Inhibitory Effect on Hep3B Liver Cancer.

Shuyi Sun, Ling Yan, Zhekai Liu +1 more · 2025 · Biomolecules · MDPI · added 2026-04-24
RNA interference (RNAi) holds promise as a gene-silencing therapy for liver cancer but faces challenges related to siRNA instability, short half-life, and inefficient cellular uptake. In this study, w Show more
RNA interference (RNAi) holds promise as a gene-silencing therapy for liver cancer but faces challenges related to siRNA instability, short half-life, and inefficient cellular uptake. In this study, we designed a self-assembling RNA nanoparticle targeting three oncogenes- Show less
📄 PDF DOI: 10.3390/biom16010045
FGFR1

Latent Profile Analysis and Influencing Factors of Medication-Related Burden in Multidrug-Resistant Tuberculosis Patients in Chengdu, China.

Yuanyuan Li, Qiaolin Yu, Rong Yao +11 more · 2025 · Patient preference and adherence · added 2026-04-24
The treatment of multidrug-resistant tuberculosis (MDR-TB) is characterized by a prolonged duration and complex medication regimens, often resulting in a substantial medication-related burden that neg Show more
The treatment of multidrug-resistant tuberculosis (MDR-TB) is characterized by a prolonged duration and complex medication regimens, often resulting in a substantial medication-related burden that negatively impacts patients' adherence and quality of life. However, research on the heterogeneity of medication-related burden among MDR-TB patients and its influencing factors remains limited. This study aimed to identify latent profiles of medication-related burden among MDR-TB patients and examine differences in burden characteristics across these profiles, thereby providing evidence for tailored intervention strategies. A convenience sampling method was employed to recruit MDR-TB patients diagnosed at a tertiary infectious disease hospital in Chengdu between December 2024 and May 2025. Data were collected using a general information questionnaire, the Living with Medicines Questionnaire (LMQ), and the Health Literacy Management Scale (HeLMS). Latent profile analysis (LPA) was conducted to identify distinct profiles of medication-related burden, and multivariate logistic regression was used to explore associated factors for each profile. A total of 214 valid responses were analyzed. The LPA identified two distinct profiles of medication-related burden: C1 - "Low-Burden (Attitude & Practice-Dominated)" (44%) and C2 - "High-Burden (Daily Interference-Dominated)" (56%). Absence of side effects, not employing a caregiver, and higher levels of health literacy were positively associated with membership in the C1 group ( Medication-related burden among MDR-TB patients exhibits clear heterogeneity. Healthcare professionals should adopt stratified management and personalized interventions based on the identified influencing factors to alleviate the burden of medication in this population. Show less
📄 PDF DOI: 10.2147/PPA.S558068
LPA

HDAC3 activates endothelial NLRP3 inflammasome and promotes atherosclerosis via inhibiting the acetylation of specificity protein 1.

Lifang Chen, Wei Zhang, Huan Chen +11 more · 2025 · Cell death and differentiation · Nature · added 2026-04-24
Histone deacetylase 3 (HDAC3) is an epigenetic modifying enzyme closely linked to the development of atherosclerosis. Endothelial inflammation is a critical factor in atherosclerosis. However, the rol Show more
Histone deacetylase 3 (HDAC3) is an epigenetic modifying enzyme closely linked to the development of atherosclerosis. Endothelial inflammation is a critical factor in atherosclerosis. However, the role of HDAC3 in mediating epigenetic modifications and regulating endothelial inflammation in atherosclerosis remains unclear. This study aims to investigate the impact of HDAC3 on endothelial inflammation and its contribution to atherosclerosis. Firstly, single-cell transcriptomic analysis identified elevated expression of HDAC3 and nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3) in inflammatory endothelial cells of atherosclerotic plaques in symptomatic patients. Endothelial-specific knockout HDAC3 in an apolipoprotein E knockout (ApoE Show less
📄 PDF DOI: 10.1038/s41418-025-01620-6
APOE

[Mechanism of elaidic acid-induced lipid accumulation in vascular smooth muscle cells].

Chenyang Xiao, Shuang Song, Jiyong Yin +4 more · 2025 · Wei sheng yan jiu = Journal of hygiene research · added 2026-04-24
To investigate the molecular mechanisms underlying EA(elaidic acid)-induced lipid accumulation in VSMCs(vascular smooth muscle cells). CCK-8 assay determined the effects of EA(0-2.8 mmol/L) on MOVAS(m Show more
To investigate the molecular mechanisms underlying EA(elaidic acid)-induced lipid accumulation in VSMCs(vascular smooth muscle cells). CCK-8 assay determined the effects of EA(0-2.8 mmol/L) on MOVAS(murine aortic vascular smooth muscle cells)to select experimental concentrations. Oil Red O staining combined with quantitative lipid droplet analysis was conducted to examine the effects of EA on intracellular lipid droplet accumulation. Intracellular total cholesterol(TC) and triglyceride(TG) levels were quantified spectrophotometrically to assess EA's effects on intracellular lipid levels. Western blot analyzed protein expression of PPARγ, LXRα, ABCA1, and ABCG1 to delineate EA's pro-foamogenic mechanism. EA dose-dependently suppressed MOVAS viability(P<0.01). EA-treated groups exhibited significant increases in lipid droplet area/number and TC/TG content versus controls(P<0.01). EA downregulated PPARγ and LXRα protein expression(P<0.05), subsequently suppressing downstream targets ABCA1 and ABCG1(P<0.05). EA disrupts lipid metabolism in VSMCs by inhibiting the PPARγ-LXRα-ABCA1/ABCG1 signaling pathway, thereby inducing lipid accumulation and promoting foam cell formation. Show less
no PDF DOI: 10.19813/j.cnki.weishengyanjiu.2025.04.021
NR1H3

Ursolic Acid Inhibits Triple-Negative Breast Cancer Progression by Modulating the FGFR1/AKT/ERK Pathway: Evidence from Network Pharmacology and Experimental Validation.

Ziming Chen, Weiqiang Guo, Yahan Gao +6 more · 2025 · Anti-cancer agents in medicinal chemistry · Bentham Science · added 2026-04-24
Ursolic acid (UA) exhibits antitumor activity; however, its effects and mechanisms on triple-negative breast cancer (TNBC) cells are not well understood. The present study aimed to explore the anti- T Show more
Ursolic acid (UA) exhibits antitumor activity; however, its effects and mechanisms on triple-negative breast cancer (TNBC) cells are not well understood. The present study aimed to explore the anti- TNBC mechanisms of UA by network pharmacology and experimental validation. TNBC cell lines MDA-MB-231 and BT-549 cells were treated with UA. A CCK-8 assay was performed to detect cell growth, while flow cytometry assessed cell cycle arrest and apoptosis. The underlying mechanism and potential targets of UA for TNBC treatment were investigated by network pharmacology, including PharmMapper database, GO, KEGG enrichment, and PPI analysis. The protein expressions and phosphorylation levels of FGFR1, AKT, and ERK were measured by western blot. Pull-down assay, cellular thermal shift assay (CETSA), and molecular docking were used to analyze the interaction between UA and FGFR1. Xenograft models were established to examine the effect of UA on TNBC tumor growth. UA effectively reduced cell viability, induced apoptosis, and arrested cell cycle in TNBC cells. Moreover, UA significantly regulated the expression of Bcl-2 and Bax to induce apoptosis. The results of network pharmacology and western blot suggested that UA reduced FGFR1/AKT/ERK pathway. Furthermore, pull-down, CETSA, and molecular docking results revealed that UA directly bound to FGFR1. In the xenograft model, UA inhibited the growth by suppressing FGFR1. In this study, we employed network pharmacology and experimental approaches to elucidate the mechanism of UA on TNBC. The results demonstrated that UA targeted FGFR1 to inhibit TNBC via mediating FGFR1/AKT/ERK pathway. Our findings demonstrate that UA inhibits the FGFR1/AKT/ERK pathway by directly targeting FGFR1, thereby suppressing TNBC progression and supporting its potential as a therapeutic agent for TNBC treatment. Show less
no PDF DOI: 10.2174/0118715206379579250722053647
FGFR1

Lipids, apolipoproteins, carbohydrates, and risk of hematological malignancies.

Qianwei Liu, Dang Wei, Niklas Hammar +6 more · 2025 · European journal of epidemiology · Springer · added 2026-04-24
Previous studies have investigated the role of metabolic factors in risk of hematological malignancies with contradicting findings. Existing studies are generally limited by potential concern of rever Show more
Previous studies have investigated the role of metabolic factors in risk of hematological malignancies with contradicting findings. Existing studies are generally limited by potential concern of reverse causality and confounding by inflammation. Therefore, we aimed to investigate the associations of glucose, lipid, and apolipoprotein biomarkers with the risk of hematological malignancy. We performed a study of over 560,000 individuals of the Swedish AMORIS cohort, with measurements of biomarkers for carbohydrate, lipid, and apolipoprotein metabolism during 1985-1996 and follow-up until 2020. We conducted a prospective cohort study and used Cox models to investigate the association of nine different metabolic biomarkers (glucose, total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), LDL-C/HDL-C, triglyceride (TG), apolipoprotein B (ApoB), apolipoprotein A-I (ApoA I), and ApoB/ApoA-I) with risk of hematological malignancy, after excluding the first five years of follow-up and adjustment for inflammatory biomarkers. We observed a decreased risk of hematological malignancy associated with one SD increase of TC (HR 0.93; 95% CI 0.91-0.96), LDL-C (HR 0.94; 95% CI 0.91-0.97), HDL-C (HR 0.92; 95% CI 0.86-0.99), and ApoA-I (HR 0.96; 95% CI 0.93-0.996). Our study highlights a decreased risk of hematological malignancy associated with a higher level of TC, LDL-C, HDL-C, and ApoA-I. Show less
📄 PDF DOI: 10.1007/s10654-025-01207-y
APOB

CD180 as a Robust Immunophenotypic Marker for Differentiating Lymphoplasmacytic Lymphoma/Waldenström Macroglobulinemia From Marginal Zone Lymphoma.

Zhen Guo, Jing Su, Lu Liu +8 more · 2025 · Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · Elsevier · added 2026-04-24
Precise differential diagnosis between lymphoplasmacytic lymphoma/Waldenström macroglobulinemia (LPL/WM) and marginal zone lymphoma (MZL) remains a challenging issue because of overlapping clinicopath Show more
Precise differential diagnosis between lymphoplasmacytic lymphoma/Waldenström macroglobulinemia (LPL/WM) and marginal zone lymphoma (MZL) remains a challenging issue because of overlapping clinicopathological and immunophenotypic features. In the present study, the differential diagnostic potential of CD180 was assessed by determining its expression patterns in patients with MZL and LPL/WM through flow cytometry. The results indicated that LPL/WM cases exhibited a complete absence of CD180 expression on malignant B cells, whereas MZL cases showed robust CD180 expression (P < .001). Receiver operating characteristic analysis demonstrated that CD180 expression percentage showed optimal diagnostic accuracy in LPL/WM and MZL cases (area under the curve = 0.998, sensitivity = 100%, and specificity = 98.0%), with a further improvement in differentiation potential by the CD180 mean fluorescence intensity ratio (lymphocytes/monocytes) of ≤ 0.47 (area under the curve = 0.937). Moreover, although the MYD88 Show less
no PDF DOI: 10.1016/j.modpat.2025.100919
LPL

CAUSAL ASSOCIATION BETWEEN SEPSIS AND FIBROBLAST GROWTH FACTORS AS WELL AS THEIR RECEPTORS LEVELS: A TWO-SAMPLE MENDELIAN RANDOMIZATION STUDY.

Junru Dai, Bangbo Xia, Ning Liu +1 more · 2025 · Shock (Augusta, Ga.) · added 2026-04-24
Objective: The potential association between sepsis risk and circulating levels of fibroblast growth factors (FGFs) and their receptors (FGFRs) has been a focus of research; however, the causal relati Show more
Objective: The potential association between sepsis risk and circulating levels of fibroblast growth factors (FGFs) and their receptors (FGFRs) has been a focus of research; however, the causal relationship between them remains to be elucidated. We hypothesize a causal association between genetically predicted FGFs, FGFRs, and sepsis risk, and we conduct a Mendelian randomization (MR) study to validate this hypothesis. Methods: We utilized a two-sample MR design to assess the effect of genetic variants associated with various FGFs (FGF1, FGF2, FGF7, FGF16, FGF19, FGF21, FGF23, FGF5) and FGFRs (FGFR1, FGFR2, FGFR3, α-Klotho) on sepsis risk, using genome-wide association study summary statistics. Our MR analyses employed the inverse-variance weighted (IVW) method, along with weighted median, weighted mode, and MR-Egger regression, supplemented by sensitivity analyses to ensure robustness. Results: The MR analysis identified an unequal number of instrumental variables ranging from 2 to 17 for FGFs and FGFRs when sepsis was the outcome. No significant correlation was found between genetically determined FGF levels and sepsis risk by IVW analysis (all P > 0.05). Correspondingly, similar nonsignificant associations were observed for FGFRs (all P > 0.05). Other MR methods corroborated the IVW findings. Sensitivity analyses, including Cochran's Q test, MR-Egger, and MR pleiotropy residual sum and outlier, indicated no significant heterogeneity or pleiotropy in the relationships, with the exception of a nonsignificant correlation between FGFR1 and sepsis that persisted after the exclusion of an outlier (odds ratio, 0.84; P = 0.34). Conclusion: The analysis found no significant causal associations between FGFs, their receptors, and sepsis risk, indicating a need for further research on their complex interactions. Show less
no PDF DOI: 10.1097/SHK.0000000000002565
FGFR1

AMPK/SIRT1/PGC-1α Signaling Pathway: Molecular Mechanisms and Targeted Strategies From Energy Homeostasis Regulation to Disease Therapy.

Junyang Chen, Boya Liu, Xinlei Yao +8 more · 2025 · CNS neuroscience & therapeutics · Blackwell Publishing · added 2026-04-24
The AMPK/SIRT1/PGC-1α pathway serves as a central regulator of cellular energy homeostasis, coordinating metabolic stress responses, epigenetic modifications, and transcriptional programs. Its dysfunc Show more
The AMPK/SIRT1/PGC-1α pathway serves as a central regulator of cellular energy homeostasis, coordinating metabolic stress responses, epigenetic modifications, and transcriptional programs. Its dysfunction is implicated in the pathogenesis of a wide spectrum of complex modern diseases, spanning neurodegeneration, metabolic syndromes, and chronic inflammatory conditions. This review examines the pathway's role as an integrative hub and its potential as a therapeutic target. We synthesize current mechanistic evidence from molecular, cellular, and preclinical studies to elucidate the pathway's operational logic and the consequences of its dysregulation. The analysis is structured around key disease paradigms-including Alzheimer's disease, Parkinson's disease, diabetes, cardiovascular injury, stroke, and chronic kidney disease-to dissect its tissue-specific pathophysiological impacts. The AMPK/SIRT1/PGC-1α axis operates through a core positive feedback loop: AMPK activation elevates NAD+, thereby activating SIRT1, which in turn deacetylates and activates PGC-1α to drive mitochondrial biogenesis and function, further reinforcing SIRT1 activity. Disruption of this cascade manifests in disease-specific mechanisms: promoting Aβ production via BACE1/γ-secretase in Alzheimer's; impairing α-synuclein clearance in Parkinson's; disrupting GLUT4 translocation and insulin signaling in diabetes; exacerbating oxidative damage and mitochondrial dysfunction in cardiovascular and neuronal injury; and accelerating fibrosis and sustained inflammation in renal and pulmonary diseases via NLRP3 and TGF-β/Smad3 signaling. The AMPK/SIRT1/PGC-1α pathway represents a cornerstone target at the intersection of metabolism, aging, and disease. Current therapeutic strategies-including pharmacological activators (e.g., metformin, SRT1720), natural compounds (e.g., resveratrol), lifestyle interventions (e.g., exercise, caloric restriction), and emerging technologies (e.g., gene editing, exosomal miRNAs)-offer multidimensional avenues for intervention. Future research must prioritize elucidating tissue-specific regulatory mechanisms, such as AMPK isoform diversity and PGC-1α interactome dynamics, to enable precision therapeutics and successful clinical translation for a range of complex disorders. Show less
📄 PDF DOI: 10.1111/cns.70657
BACE1

Endothelial cells-derived SEMA3G suppresses glioblastoma stem cells by inducing c-Myc degradation.

Peng-Xiang Min, Li-Li Feng, Yi-Xuan Zhang +12 more · 2025 · Cell death and differentiation · Nature · added 2026-04-24
The poor prognosis of glioblastoma (GBM) patients is attributed mainly to abundant neovascularization and presence of glioblastoma stem cells (GSCs). GSCs are preferentially localized to the perivascu Show more
The poor prognosis of glioblastoma (GBM) patients is attributed mainly to abundant neovascularization and presence of glioblastoma stem cells (GSCs). GSCs are preferentially localized to the perivascular niche to maintain stemness. However, the effect of abnormal communication between endothelial cells (ECs) and GSCs on GBM progression remains unknown. Here, we reveal that ECs-derived SEMA3G, which is aberrantly expressed in GBM patients, impairs GSCs by inducing c-Myc degradation. SEMA3G activates NRP2/PLXNA1 in a paracrine manner, subsequently inducing the inactivation of Cdc42 and dissociation of Cdc42 and WWP2 in GSCs. Once released, WWP2 interacts with c-Myc and mediates c-Myc degradation via ubiquitination. Genetic deletion of Sema3G in ECs accelerates GBM growth, whereas SEMA3G overexpression or recombinant SEMA3G protein prolongs the survival of GBM bearing mice. These findings illustrate that ECs play an intrinsic inhibitory role in GSCs stemness via the SMEA3G-c-Myc distal regulation paradigm. Targeting SEMA3G signaling may have promising therapeutic benefits for GBM patients. Show less
no PDF DOI: 10.1038/s41418-025-01534-3
WWP2

LncRNA A2ml2 inhibits fatty liver hemorrhage syndrome progression and function as ceRNA to target LPL by sponging miR-143-5p.

Qingxing Xiao, Sibao Yang, Yuwei Yang +7 more · 2025 · Poultry science · Elsevier · added 2026-04-24
Fatty liver hemorrhage syndrome (FLHS) is the most common metabolic diseases in laying hens during the late-laying period, and it causes a significant economic burden on the poultry industry. The comp Show more
Fatty liver hemorrhage syndrome (FLHS) is the most common metabolic diseases in laying hens during the late-laying period, and it causes a significant economic burden on the poultry industry. The competing endogenous RNA plays crucial roles in the occurrence and development of fatty liver. Based on the previously constructed lncRNA-miRNA-mRNA networks, we selected the axis of ENSGALT00000079786-LPL-miR-143-5p for further study to elucidate its mechanistic role in development of fatty liver. In this study, we identified a novel highly conserved lncRNA (ENSGALT00000079786) in poultry, which we designated as lncRNA A2ml2 based on its chromosomal location. Fluorescent in situ hybridization (FISH) revealed that lncRNA A2ml2 was localized in both the nucleus and cytoplasm. Dual-luciferase reporter assay validated the targeted relationship between lncRNA A2ml2, miR-143-5p, and the LPL gene. To further analyze the lncRNA A2ml2 and miR-143-5p function, lncRNA A2ml2 overexpression vector was successfully constructed and transfected into Leghorn male hepatocellular (LMH) cells, which could remarkably inhibit cellular lipid deposition was detected by oil red staining (P < 0.01), the opposite occurred for miR-143-5p (P < 0.01). qPCR demonstrated an inverse correlation between miR-143-5p expression and lncRNA A2ml2 expression, and confirmed that miR-143-5p directly target lncRNA A2ml2. Similarly, we found an inverse correlation between expression of LPL and the expression of miR-143-5p. To further investigate the interactions among these three factors and their effects on cellular lipid metabolism, we assessed the expression levels of LPL by co-transfecting lncRNA A2ml2 with miR-143-5p mimic and miR-143-5p mimic binding site mutants. Co-transfection experiments showed that miR-143-5p diminished the promoting effect of lncRNA A2ml2 on LPL. Meanwhile, miR-143-5p has the capacity to mitigate the suppressive impact of lncRNA A2ml2 overexpression on lipid accumulation in LMH cells. The results revealed that lncRNA A2ml2 attenuated hepatic lipid accumulation through negatively regulating miR-143-5p and enhancing LPL expression in LMH cells. Our findings offer novel insights into ceRNA-mediated in FLHS and identify a novel lncRNA as a potential molecular biomarker. Show less
📄 PDF DOI: 10.1016/j.psj.2025.105003
LPL

Blood and urine biomarkers and myocardial infarction: A 2-sample and multivariate combination of Mendelian randomization.

Yu Ding, Haoyang Ling, Xiuyan Chen +6 more · 2025 · Medicine · added 2026-04-24
Myocardial infarction (MI) is one of the most serious cardiovascular diseases in the world. Nevertheless, the majority of diagnostic procedures conducted subsequent to the illness do not provide any m Show more
Myocardial infarction (MI) is one of the most serious cardiovascular diseases in the world. Nevertheless, the majority of diagnostic procedures conducted subsequent to the illness do not provide any means to prevent several risks associated with MI. Blood and urine tests are frequently employed in clinical examinations to detect cardiovascular diseases at an early stage. Mendelian randomization (MR) is commonly employed to explore disease-trait relationships and uncover therapeutic targets. Our goal was to explore the genetic links between 35 blood and urine biomarkers and MI. Blood and urine biomarker MR correlations with MI risk were studied. In version R10, the UK Biobank and Finnish databases included blood and urine marker data and MI data (26,060 cases and 343,079 controls). We performed bidirectional 2-sample MR with 4 methods: inverse variance weighted, MR-Egger, weighted median, and weighted mode. Final causal associations were determined by inverse variance weighted. Sensitivity analyses (heterogeneity, pleiotropy) were conducted. MR-PRESSO and PhenoScanner were used to exclude invalid instruments. We used multivariate MR to filter the most important genes without including other positive genes. To identify positive gene pathways and gene networks that cause MI, we employed GeneMANIA for gene prediction. The findings revealed a positive genetic association between the 8 blood and urine biomarker levels and an elevated risk of MI. There are apolipoprotein B (APOB), glycated hemoglobin, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, sex hormone-binding globulin, triglycerides, and urate. Moreover, APOB, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol selectively affect MI through the rejection of other positive gene stems. Finally, APOB and numerous genes strongly impact MI development. APOB collaborates with related genes to regulate plasma lipoprotein particle levels, sterol homeostasis, organization, lipid homeostasis, and remodeling in MI. Our research further reveals the causal relationship between MI and blood/urine biomarkers, providing a new perspective for the prevention, diagnosis, and treatment of MI. Blood and urine marker tests can subsequently be conducted based on these results to detect MI and study the underlying mechanisms linking these metabolites to MI. Show less
no PDF DOI: 10.1097/MD.0000000000046146
APOB

NSP6 regulates calcium overload-induced autophagic cell death and is regulated by KLHL22-mediated ubiquitination.

Xingyu Tao, Yanan Wang, Jiangbo Jin +10 more · 2025 · Journal of advanced research · Elsevier · added 2026-04-24
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a substantial global threat. SARS-CoV-2 nonstructural proteins (NSPs) are essential for impeding the host replication mechanism while Show more
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a substantial global threat. SARS-CoV-2 nonstructural proteins (NSPs) are essential for impeding the host replication mechanism while also assisting in the production and organization of new viral components. However, NSPs are not incorporated into viral particles, and their subsequent fate within host cells remains poorly understood. Additionally, their role in viral pathogenesis requires further investigation. This study aimed to discover the ultimate fate of NSP6 in host cells and to elucidate its role in viral pathogenesis. We investigated the effects of NSP6 on cell death and explored the underlying mechanism; moreover, we examined the degradation mechanism of NSP6 in human cells, along with analysing its correlation with coronavirus disease 2019 (COVID-19) severity in patient peripheral blood mononuclear cells (PBMCs). NSP6 was demonstrated to induce cell death. Specifically, NSP6 interacted with EI24 autophagy-associated transmembrane protein (EI24) to increase intracellular Ca This study reveals that KLHL22-mediated ubiquitination controls NSP6 stability and that NSP6 induces autophagic cell death via calcium overload, highlighting its cytotoxic role and suggesting therapeutic strategies that target calcium signaling or promote NSP6 degradation as potential interventions against COVID-19. Show less
no PDF DOI: 10.1016/j.jare.2025.05.031
PIK3C3

Single nucleotide polymorphisms in ovarian cancer impacting lipid metabolism and prognosis: an integrated TCGA database analysis.

Haoyu Wang, Tian Tu, Lijun Yin +2 more · 2025 · BMC cancer · BioMed Central · added 2026-04-24
Ovarian cancer (OC) stands as a formidable adversary among women, remaining a leading cause of cancer-related mortality owing to its aggressive and invasive nature. Investigating prognostic markers in Show more
Ovarian cancer (OC) stands as a formidable adversary among women, remaining a leading cause of cancer-related mortality owing to its aggressive and invasive nature. Investigating prognostic markers intricately linked to OC's molecular pathogenesis represents a critical avenue for enhancing patient outcomes and survival prospects. In this comprehensive study, we embarked on a bioinformatics journey, leveraging the vast repository of single nucleotide polymorphism (SNP) data from OC patients available within the TCGA database. Our overarching goal was to unearth the genetic underpinnings of OC, shedding light on potential prognostic markers that could significantly impact clinical decision-making and patient care. Our meticulous analysis led to the discovery of five mutated genes-APOB, BRCA1, COL6A3, LRP1, and LRP1B-engaged in the intricate world of lipid metabolism. These genes, previously unexplored in the context of OC, emerged as prominent figures in our investigation, showcasing their potential roles in OC progression. The intricate interplay between lipid metabolism and cancer development has garnered considerable attention in recent years, and our findings underscore the relevance of these genes in the context of OC. To fortify our discoveries, we delved into the realm of survival analysis, a pivotal component of our investigation. The results yielded compelling evidence of significant correlations between patient survival and the expression levels of the aforementioned genes. This critical insight underscores the potential utility of these genes as prognostic markers, illuminating a path toward more personalized and effective approaches to patient care. Our study represents a multifaceted approach to unraveling the complex molecular pathogenesis of OC. By harnessing the power of high-throughput data mining, we uncovered genetic insights that may reshape our understanding of this formidable disease. We complemented these findings with advanced techniques such as RT-qPCR and Western blot, further dissecting the intricacies of OC's molecular landscape. This holistic approach not only deepens our understanding but also provides essential bioinformatics information that holds promise in assessing patient prognosis. In summary, our study represents a significant stride in the quest to decode the molecular intricacies of ovarian cancer. Our findings spotlight the potential prognostic significance of APOB, BRCA1, COL6A3, LRP1, and LRP1B, inviting further exploration into their roles in OC progression. Ultimately, our research carries the potential to shape the future of OC management, offering a glimpse into a more personalized and effective approach to patient care. Show less
📄 PDF DOI: 10.1186/s12885-025-13841-6
APOB

PNPLA3-I148M is a neomorph that interferes with two primary hepatic triglyceride clearance pathways.

David J Sherman, Lei Liu, Edward L LaGory +8 more · 2025 · Cell reports · Elsevier · added 2026-04-24
The common variant PNPLA3-I148M, globally, is the most significant genetic risk factor for fatty liver disease. However, it is unclear precisely how I148M drives disease risk. Using human hepatoma cel Show more
The common variant PNPLA3-I148M, globally, is the most significant genetic risk factor for fatty liver disease. However, it is unclear precisely how I148M drives disease risk. Using human hepatoma cells expressing endogenous I148M, we find that the variant impairs cellular secretion of apolipoprotein B (ApoB), the scaffolding protein of very-low-density lipoprotein (VLDL). This is not due to loss-of-function of wild-type PNPLA3. Expression of human I148M in primary hepatocytes and mice also hinders VLDL secretion. Lipidomic profiling reveals a shift from polyunsaturated phosphatidylcholine to polyunsaturated triglycerides in I148M cells, reducing membrane fluidity and, concomitantly, VLDL biogenesis. ApoB secretion is substantially rescued in I148M cells overexpressing ABHD5/CGI-58, an I148M-binding partner that normally activates ATGL/PNPLA2-mediated triglyceride lipolysis. Conversely, knocking down CGI-58 or PNPLA2 mimics I148M. We propose that I148M is a neomorph that exacerbates fatty liver risk by simultaneously impeding two major CGI-58-dependent pathways for liver triglyceride clearance: lipolysis and secretion. Show less
no PDF DOI: 10.1016/j.celrep.2025.116371
APOB

Transcriptomic and Proteomic Insights Into Buffalo Milk Fat Synthesis and the Role of IGFBP4 in BMECs.

Xinyue Shen, Chaobin Qin, Zhixiang Wang +5 more · 2025 · FASEB journal : official publication of the Federation of American Societies for Experimental Biology · added 2026-04-24
The content and composition of milk fat are critical determinants influencing milk flavor, nutritional value, and economic significance. Buffalo milk is characterized by its high-fat content and compl Show more
The content and composition of milk fat are critical determinants influencing milk flavor, nutritional value, and economic significance. Buffalo milk is characterized by its high-fat content and complex lipid profile, characterized by elevated levels of health-beneficial fatty acids such as linoleic acid, α-linolenic acid, and arachidonic acid. However, the molecular regulatory mechanisms governing milk fat synthesis in buffaloes remain incompletely elucidated. This study employed transcriptomic analysis of milk fat globules (MFGs) from buffaloes exhibiting high and low milk fat content, identifying 15 949 annotated genes, including 234 differentially expressed genes (DEGs). Functional enrichment analysis revealed that these DEGs were predominantly associated with cell proliferation and differentiation, glyconeogenesis, and reproductive system development. Notably, the expression of IGFBP4, AGPAT4, GPAT3, GPR84, and PC exhibited positive correlations with buffalo milk fat content, identifying them as potential candidate genes regulating milk fat synthesis. Proteomic profiling identified 1678 proteins, including 53 differentially expressed proteins (DEPs). Enrichment analysis indicated that DEPs were primarily involved in nucleotide metabolism, the tricarboxylic acid (TCA) cycle, glycerophospholipid metabolism, and TGF-β signaling. Integrated analysis revealed potential interactions involving the IGFBP4 and PC genes, as well as the ACO1, TMED7, and APRT proteins, highlighting IGFBP4 as a pivotal regulator of milk fat synthesis. Functional validation demonstrated that overexpression or knockdown of IGFBP4 in buffalo mammary epithelial cells (BMECs) significantly modulated cell proliferation and altered the expression of key milk fat synthesis-related genes (FABP3, LPL, SCD, ACACA, and FASN), indicating that IGFBP4 can promote de novo fatty acid synthesis and intracellular lipid storage while inhibiting exogenous fatty acid uptake. Collectively, this study provides novel mechanistic insights into the regulation of milk fat synthesis in buffaloes and establishes a foundation for enhancing lactation traits through targeted genetic breeding strategies. Show less
📄 PDF DOI: 10.1096/fj.202502191R
LPL

Orlistat Confers Neuroprotection in Traumatic Brain Injury by Modulating Microglial Lipid Metabolism.

Chenxuan Yu, Yu Ni, Yuxuan Xiong +9 more · 2025 · Cells · MDPI · added 2026-04-24
Traumatic brain injury (TBI) represents a major cause of mortality and disability worldwide, particularly affecting young adults and elderly populations. This study investigates the neuroprotective po Show more
Traumatic brain injury (TBI) represents a major cause of mortality and disability worldwide, particularly affecting young adults and elderly populations. This study investigates the neuroprotective potential of orlistat (ORL), a gastrointestinal lipase inhibitor, in a murine TBI model. Behavioral, histological, and molecular analyses demonstrated that ORL significantly attenuated TBI-induced neurological damage. Microglial depletion experiments revealed that ORL's neuroprotective effects were largely mediated through microglial modulation. In vitro and in vivo studies showed that ORL suppressed microglial activation, phagocytosis, and migration. Single-cell RNA sequencing identified upregulation of lipoprotein lipase (LPL) in a TBI-induced microglial subpopulation. Molecular docking predicted ORL-LPL binding, suggesting direct enzymatic inhibition. Transcriptomic and metabolomic analyses further revealed ORL's modulation of microglial metabolic pathways and inflammatory responses. Our findings position ORL as a promising repurposed therapeutic for TBI through its novel mechanism of targeting microglial LPL-mediated neuroinflammation. Show less
📄 PDF DOI: 10.3390/cells14181469
LPL

CCL26-CX3CR1 Axis Mediates a Feedback Loop between Cancer Cell and PMN-MDSCs to Promote CD8

Xiaotao Jiang, Hui Wu, Ning Yan +14 more · 2025 · Research (Washington, D.C.) · added 2026-04-24
The development of an immunosuppressive microenvironment is a critical factor in stomach carcinogenesis. Polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs) serve a pivotal function in medi Show more
The development of an immunosuppressive microenvironment is a critical factor in stomach carcinogenesis. Polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs) serve a pivotal function in mediating immune suppression. However, the precise mechanisms underlying PMN-MDSCs infiltration into the tumor immune microenvironment (TIME) and their immunosuppressive functions remain poorly understood. In this investigation, we observed that PMN-MDSCs were up-regulated during stomach carcinogenesis, with gastric cancer (GC) cells secreting CCL26 to promote the infiltration of PMN-MDSCs into the TIME via the CX3CR1 receptor. The infiltrating CX3CR1 Show less
no PDF DOI: 10.34133/research.1002
SNAI1

Blood lipid profiles and mood disorders: A principal component analysis of UK Biobank data reveals distinct associations with depression and bipolar disorder.

Xiangliang Liu, Yuguang Li, Wang Yang +5 more · 2025 · Journal of affective disorders · Elsevier · added 2026-04-24
Growing evidence suggests that lipid metabolism may play a crucial role in mood disorder pathophysiology, and the correlation between blood lipids and mood disorder remains further clarified. This pro Show more
Growing evidence suggests that lipid metabolism may play a crucial role in mood disorder pathophysiology, and the correlation between blood lipids and mood disorder remains further clarified. This prospective, population-based cohort study utilized data from the UK Biobank. The study included 268,098 and 292,121 participants who had never been diagnosed with depression or bipolar disorder and who had complete data at both the baseline and follow-up points. A principal component analysis (PCA) was conducted on seven blood lipids, and the first three principal components (PCs) were derived. Cox regression analysis was employed to examine the correlation between the risk of mood disorders and the PCs. Multiplicative interaction and sensitivity analyses were also conducted. The relationship between blood lipids and neurological biomarkers was explored using Spearman's analysis. PC1, primarily reflecting levels of Apolipoprotein B (ApoB), cholesterol, and low-density lipoprotein cholesterol (LDL-C), showed a protective effect against depression, with HRs of 0.98 (95 % CI: 0.96,1.00) in the fully adjusted Cox regression model. In contrast, PC2, characterized by opposite loadings for triglycerides and high-density lipoprotein cholesterol (HDLC), was positively associated with the risk of depression and bipolar disorder.(HR = 1.03,95 % CI: 1.01,1.06; HR = 1.11, 95 % CI: 1.01,1.23). Increased PC2 level was related to a significant increase in bipolar disorder risk among participants with high genetic risk (genetic risk score > 90 %, HR = 1.22, 95 % CI: 1.02,1.46). Complicated correlations between blood lipids and serum neuroproteins were detected. These findings suggest complex associations between blood lipid profiles and the risk of depression and bipolar disorder. Show less
no PDF DOI: 10.1016/j.jad.2025.02.040
APOB

Notch2 Inhibition and Kidney Cyst Growth in Autosomal Dominant Polycystic Kidney Disease.

Huiwen Ren, Chengsen Mu, Yuhan Wang +10 more · 2025 · Journal of the American Society of Nephrology : JASN · added 2026-04-24
Notch2 activation promotes kidney cyst growth. Silencing Notch2 ameliorated cyst growth in mice with autosomal dominant polycystic kidney disease. Notch signaling, a conserved mechanism of cell-to-cel Show more
Notch2 activation promotes kidney cyst growth. Silencing Notch2 ameliorated cyst growth in mice with autosomal dominant polycystic kidney disease. Notch signaling, a conserved mechanism of cell-to-cell communication, plays a crucial role in regulating cellular processes, such as proliferation and differentiation, in a context-dependent manner. However, the specific contribution of Notch signaling to the progression of polycystic kidney disease (PKD) remains unclear. We investigated the changes in Notch signaling activity (Notch1–4) in the kidneys of patients with autosomal dominant PKD (ADPKD) and two ADPKD mouse models (early and late onset). Multiple genetic and pharmacologic approaches were used to explore Notch2 signaling during kidney cyst formation in PKD. Notch2 expression was significantly increased in the kidney tissues of patients with ADPKD and ADPKD mice. Targeted expression of Notch2 intracellular domain in renal epithelial cells resulted in cyst formation and kidney failure in neonatal and adult mice. Mechanistically, Notch2/Hey2 signaling promoted renal epithelial cell proliferation by driving the expression of the E26 transformation–specific homologous factor (Ehf). Depletion of Ehf delayed Notch2 intracellular domain overexpression–induced cyst formation and kidney failure in mice. A gain-of-function mutation in exon 34 of Notch2 signaling promoted kidney cyst growth, partially by upregulating Ehf expression. Show less
no PDF DOI: 10.1681/ASN.0000000592
HEY2